1 | import json
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2 | import logging
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3 | import math
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4 | import os.path
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5 | import random
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6 | from decimal import Decimal
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7 | from random import randint
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8 | from time import time
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9 | from typing import cast
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10 | from typing import final
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11 |
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12 | import geniusweb.actions.LearningDone
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13 | from geniusweb.actions.Accept import Accept
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14 | from geniusweb.actions.Action import Action
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15 | from geniusweb.actions.Offer import Offer
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16 | from geniusweb.actions.PartyId import PartyId
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17 | from geniusweb.bidspace.AllBidsList import AllBidsList
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18 | from geniusweb.inform.ActionDone import ActionDone
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19 | from geniusweb.inform.Finished import Finished
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20 | from geniusweb.inform.Inform import Inform
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21 | from geniusweb.inform.Settings import Settings
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22 | from geniusweb.inform.YourTurn import YourTurn
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23 | from geniusweb.issuevalue import DiscreteValue, NumberValue
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24 | from geniusweb.issuevalue.Bid import Bid
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25 | from geniusweb.issuevalue.Domain import Domain
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26 | from geniusweb.party.Capabilities import Capabilities
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27 | from geniusweb.party.DefaultParty import DefaultParty
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28 | from geniusweb.profile.utilityspace import UtilitySpace
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29 | from geniusweb.bidspace.AllBidsList import AllBidsList
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30 | from geniusweb.profile.utilityspace.LinearAdditiveUtilitySpace import (
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31 | LinearAdditiveUtilitySpace,
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32 | )
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33 | from geniusweb.profileconnection.ProfileConnectionFactory import (
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34 | ProfileConnectionFactory,
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35 | )
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36 | from geniusweb.profileconnection.ProfileInterface import (
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37 | ProfileInterface
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38 | )
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39 | from geniusweb.progress.ProgressRounds import ProgressRounds
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40 | from geniusweb.progress.ProgressTime import ProgressTime
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41 | from geniusweb.references.Parameters import Parameters
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42 | from tudelft_utilities_logging.ReportToLogger import ReportToLogger
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43 |
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44 | from agents.template_agent.utils.opponent_model import OpponentModel
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45 |
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46 |
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47 | class SmartAgent(DefaultParty):
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48 | def __init__(self):
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49 | super().__init__()
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50 |
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51 | self.all_bid_list = None
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52 | self.logger: ReportToLogger = self.getReporter()
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53 |
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54 | self.domain: Domain = None
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55 | self.parameters: Parameters = None
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56 | self.profile: LinearAdditiveUtilitySpace = None
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57 | self.profileInt: ProfileInterface = None
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58 | self.progress: ProgressTime = None
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59 | self.me: PartyId = None
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60 | self.random: final(random) = random.Random()
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61 | self.protocol = ""
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62 | self.opponent_name: str = None
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63 | self.settings: Settings = None
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64 | self.storage_dir: str = None
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65 |
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66 | self.time_split = 40
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67 | self.time_phase = 0.2
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68 | self.new_weight = 0.3
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69 | self.smooth_width = 3
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70 | self.opponent_decrease = 0.65
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71 | self.default_alpha = 10.7
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72 | self.alpha = self.default_alpha
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73 |
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74 | self.opponent_avg_utility = 0.0
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75 | self.opponent_negotiations = 0
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76 | self.opponent_avg_max_utility = {}
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77 | self.opponent_encounters = {}
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78 |
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79 | self.std_utility = 0.0
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80 | self.negotiation_results = []
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81 | self.avg_opponent_utility = {}
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82 | self.opponent_alpha = {}
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83 | self.opponent_sum = [0.0] * 5000
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84 | self.opponent_counter = [0.0] * 5000
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85 |
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86 | self.persistent_state = {"opponent_alpha": self.default_alpha, "avg_max_utility": 0.0}
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87 | self.negotiation_data = {"aggreement_util": 0.0, "max_received_util": 0.0, "opponent_name": "", "opponent_util": 0.0,
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88 | "opponent_util_by_time": [0.0] * self.time_split}
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89 | self.opponent_utility_by_time = self.negotiation_data["opponent_util_by_time"]
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90 | self.need_to_read_persistent_data = True
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91 | self.freqMap = {}
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92 | self.MAX_SEARCHABLE_BIDSPACE = 50000
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93 | self.utilitySpace: UtilitySpace = None
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94 | self.all_bid_list: AllBidsList
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95 | self.optimalBid: Bid = None
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96 | self.bestOfferedBid: Bid = None
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97 | self.utilThreshold = None
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98 | self.opThreshold = None
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99 | self.last_received_bid: Bid = None
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100 | self.opponent_model: OpponentModel = None
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101 | self.logger.log(logging.INFO, "party is initialized")
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102 |
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103 | def notifyChange(self, data: Inform):
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104 | """MUST BE IMPLEMENTED
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105 | This is the entry point of all interaction with your agent after is has been initialised.
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106 | How to handle the received data is based on its class type.
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107 |
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108 | Args:
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109 | info (Inform): Contains either a request for action or information.
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110 | """
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111 |
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112 | # a Settings message is the first message that will be send to your
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113 | # agent containing all the information about the negotiation session.
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114 | try:
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115 | if isinstance(data, Settings):
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116 | # data is an object that is passed at the start of the negotiation
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117 | self.settings = cast(Settings, data)
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118 | # ID of my agent
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119 | self.me = self.settings.getID()
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120 |
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121 | # progress towards the deadline has to be tracked manually through the use of the Progress object
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122 | self.progress = self.settings.getProgress()
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123 |
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124 | self.protocol = self.settings.getProtocol().getURI().getPath()
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125 | self.parameters = self.settings.getParameters()
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126 | self.storage_dir = self.parameters.get("storage_dir")
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127 |
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128 | # TODO: Add persistance
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129 | # the profile contains the preferences of the agent over the domain
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130 | profile_connection = ProfileConnectionFactory.create(
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131 | data.getProfile().getURI(), self.getReporter()
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132 | )
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133 | self.profile = profile_connection.getProfile()
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134 | self.domain = self.profile.getDomain()
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135 |
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136 | if str(self.settings.getProtocol().getURI()) == "Learn":
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137 | self.learn()
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138 | self.getConnection().send(geniusweb.actions.LearningDone.LearningDone)
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139 | else:
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140 | # This is the negotiation step
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141 | try:
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142 | self.profileInt = ProfileConnectionFactory.create(self.settings.getProfile().getURI(),
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143 | self.getReporter())
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144 | domain = self.profileInt.getProfile().getDomain()
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145 |
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146 | if self.freqMap != {}:
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147 | self.freqMap.clear()
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148 | issues = domain.getIssues()
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149 | for s in issues:
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150 | pair = ({}, {})
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151 | vlist = pair[1]
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152 | vs = domain.getValues(s)
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153 | if isinstance(vs.get(0), DiscreteValue.DiscreteValue.__class__):
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154 | pair.type = 0
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155 | elif isinstance(vs.get(0), NumberValue.NumberValue.__class__):
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156 | pair.type = 1
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157 | for v in vs:
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158 | vlist[str(v)] = 0
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159 | self.freqMap[s] = pair
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160 | self.utilitySpace: UtilitySpace.UtilitySpace = self.profileInt.getProfile()
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161 | self.all_bid_list = AllBidsList(domain)
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162 |
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163 | bids_zise = self.all_bid_list.size()
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164 | if bids_zise < self.MAX_SEARCHABLE_BIDSPACE:
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165 | r = -1
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166 | elif bids_zise == self.MAX_SEARCHABLE_BIDSPACE:
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167 | r = 0
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168 | else:
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169 | r = 1
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170 | if r == 0 or r == -1:
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171 | mx_util = 0
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172 | bidspace_size = self.all_bid_list.size()
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173 | for i in range(0, bidspace_size, 1):
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174 | b: Bid = self.all_bid_list.get(i)
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175 | candidate = self.utilitySpace.getUtility(b)
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176 | r = candidate.compare(mx_util)
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177 | if r == 1:
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178 | mx_util = candidate
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179 | self.optimalBid = b
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180 | else:
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181 | # Searching for best bid in random subspace
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182 | mx_util = 0
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183 | for attempt in range(0,self.MAX_SEARCHABLE_BIDSPACE,1):
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184 | irandom = random.random(self.all_bid_list.size())
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185 | b = self.all_bid_list.get(irandom)
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186 | candidate = self.utilitySpace.getUtility(b)
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187 | r = candidate.compare(mx_util)
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188 | if r == 1:
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189 | mx_util = candidate
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190 | self.optimalBid = b
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191 | except:
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192 | raise Exception("Illegal state exception")
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193 | profile_connection.close()
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194 | # ActionDone informs you of an action (an offer or an accept)
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195 | # that is performed by one of the agents (including yourself).
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196 | elif isinstance(data, ActionDone):
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197 | action = cast(ActionDone, data).getAction()
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198 | actor = action.getActor()
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199 | # ignore action if it is our action
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200 | if actor != self.me:
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201 | # obtain the name of the opponent, cutting of the position ID.
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202 | self.opponent_name = str(actor).rsplit("_", 1)[0]
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203 | if self.need_to_read_persistent_data:
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204 | self.negotiation_data = self.read_persistent_negotiation_data()
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205 | self.need_to_read_persistent_data = False
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206 | self.negotiation_data["opponent_name"] = self.opponent_name
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207 | self.opThreshold = self.getSmoothThresholdOverTime(self.opponent_name)
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208 | if self.opThreshold is not None:
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209 | for i in range(1, self.time_split, 1):
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210 | if self.opThreshold[i] < 0:
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211 | self.opThreshold[i] = self.opThreshold[i - 1]
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212 | self.alpha = self.persistent_state["opponent_alpha"]
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213 | if self.alpha < 0.0:
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214 | self.alpha = self.default_alpha
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215 | self.update_negotiation_data()
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216 |
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217 | # process action done by opponent
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218 | self.opponent_action(action)
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219 |
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220 | # YourTurn notifies you that it is your turn to act
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221 | elif isinstance(data, YourTurn):
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222 | if isinstance(self.progress, ProgressRounds):
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223 | self.progress = cast(ProgressRounds, self.progress).advance()
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224 | self.my_turn()
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225 | # Finished will be send if the negotiation has ended (through agreement or deadline)
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226 | elif isinstance(data, Finished):
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227 | self.negotiation_data["aggreement_util"] = float(self.utilitySpace.getUtility(self.last_received_bid))
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228 | self.negotiation_data["opponent_util"] = self.calc_opponnets_value(self.last_received_bid)
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229 | self.update_opponents_offers(self.opponent_sum, self.opponent_counter)
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230 | self.save_data()
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231 | # terminate the agent MUST BE CALLED
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232 | self.logger.log(logging.INFO, "party is terminating:")
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233 | super().terminate()
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234 | else:
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235 | self.logger.log(logging.WARNING, "Ignoring unknown info " + str(data))
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236 | except:
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237 | raise Exception("Illegal state exception")
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238 |
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239 | def getCapabilities(self) -> Capabilities:
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240 | """MUST BE IMPLEMENTED
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241 | Method to indicate to the protocol what the capabilities of this agent are.
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242 | Leave it as is for the ANL 2022 competition
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243 |
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244 | Returns:
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245 | Capabilities: Capabilities representation class
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246 | """
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247 | return Capabilities(
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248 | set(["SAOP", "Learn"]),
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249 | set(["geniusweb.profile.utilityspace.LinearAdditive"]),
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250 | )
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251 |
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252 | def send_action(self, action: Action):
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253 | """Sends an action to the opponent(s)
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254 |
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255 | Args:
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256 | action (Action): action of this agent
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257 | """
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258 | self.getConnection().send(action)
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259 |
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260 | # give a description of your agent
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261 | def getDescription(self) -> str:
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262 | """MUST BE IMPLEMENTED
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263 | Returns a description of your agent. 1 or 2 sentences.
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264 |
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265 | Returns:
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266 | str: Agent description
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267 | """
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268 | return "Smart agent for the ANL 2022 competition"
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269 |
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270 | def update_frequency_map(self, bid):
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271 | if bid is not None:
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272 | issues = bid.getIssues()
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273 | for s in issues:
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274 | p = self.freqMap.get(s)
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275 | v = bid.getValue(s)
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276 | vList = p[1]
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277 | vList[str(v)] += 1
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278 |
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279 | def opponent_action(self, action):
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280 | """Process an action that was received from the opponent.
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281 |
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282 | Args:
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283 | action (Action): action of opponent
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284 | """
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285 | # if it is an offer, set the last received bid
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286 | if isinstance(action, Offer):
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287 | # create opponent model if it was not yet initialised
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288 | if self.opponent_model is None:
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289 | self.opponent_model = OpponentModel(self.domain)
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290 |
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291 | bid = cast(Offer, action).getBid()
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292 | # update opponent model with bid
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293 | self.opponent_model.update(bid)
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294 | self.update_negotiation_data()
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295 | # set bid as last received
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296 | self.last_received_bid = bid
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297 | self.update_frequency_map(self.last_received_bid)
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298 | utilVal = self.utilitySpace.getUtility(bid)
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299 | self.negotiation_data["max_received_util"] = float(utilVal)
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300 | if isinstance(action, Accept):
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301 | self.last_received_bid = self.optimalBid
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302 | def my_turn(self):
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303 | """This method is called when it is our turn. It should decide upon an action
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304 | to perform and send this action to the opponent.
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305 | """
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306 | if self.is_near_negotiation_end() > 0:
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307 | index = int((self.time_split - 1) / (1 - self.time_phase) * (self.progress.get(int(time() * 1000)) - self.time_phase))
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308 | if self.opponent_sum[index]:
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309 | self.opponent_sum[index] = self.calc_opponnets_value(self.last_received_bid)
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310 | else:
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311 | self.opponent_sum[index] += self.calc_opponnets_value(self.last_received_bid)
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312 | self.opponent_counter[index] += 1
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313 | # check if the last received offer is good enough
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314 | if self.accept_condition(self.last_received_bid):
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315 | # if so, accept the offer
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316 | action = Accept(self.me, self.last_received_bid)
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317 | else:
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318 | # if not, find a bid to propose as counter offer
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319 | bid: Bid = None
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320 |
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321 | if self.bestOfferedBid is None:
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322 | self.bestOfferedBid = self.last_received_bid
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323 | elif self.utilitySpace.getUtility(self.last_received_bid) > self.utilitySpace.getUtility(
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324 | self.bestOfferedBid):
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325 | self.bestOfferedBid = self.last_received_bid
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326 | if self.is_near_negotiation_end() == 0:
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327 | for attempt in range(0, 1000, 1):
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328 | if not self.accept_condition(bid):
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329 | i = random.randint(0, self.all_bid_list.size())
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330 | bid = self.all_bid_list.get(i)
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331 | if self.accept_condition(bid):
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332 | bid = bid
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333 | else:
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334 | bid = self.optimalBid
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335 |
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336 | else:
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337 | for attempt in range(0, 1000, 1):
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338 | if bid != self.optimalBid and not self.accept_condition(bid) and not self.is_opponents_proposal_is_good(bid):
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339 | i = random.randint(0, self.all_bid_list.size())
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340 | bid = self.all_bid_list.get(i)
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341 | if self.progress.get(int(time()) * 1000) > 0.99 and self.accept_condition(self.bestOfferedBid):
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342 | bid = self.bestOfferedBid
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343 | if not self.accept_condition(bid):
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344 | bid = self.optimalBid
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345 | action = Offer(self.me, bid)
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346 |
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347 | # send the action
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348 | self.send_action(action)
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349 |
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350 | def read_persistent_negotiation_data(self):
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351 | if os.path.exists(f"{self.storage_dir}/{self.opponent_name}"):
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352 | with open(f"{self.storage_dir}/{self.opponent_name}", "r") as f:
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353 | data = json.load(f)
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354 | return data
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355 | else:
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356 | return {"opponent_alpha": self.default_alpha, "aggreement_util": 0.0, "max_received_util": 0.0,
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357 | "opponent_name": self.opponent_name,
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358 | "opponent_util": 0.0,
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359 | "opponent_util_by_time": [0.0] * self.time_split}
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360 |
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361 | def save_data(self):
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362 | """This method is called after the negotiation is finished. It can be used to store data
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363 | for learning capabilities. Note that no extensive calculations can be done within this method.
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364 | Taking too much time might result in your agent being killed, so use it for storage only.
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365 | """
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366 | with open(f"{self.storage_dir}/{self.opponent_name}", "w") as f:
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367 | f.write(json.dumps(self.negotiation_data))
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368 |
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369 | def is_near_negotiation_end(self):
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370 | prog = self.progress.get(time() * 1000)
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371 | if prog < self.time_phase:
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372 | return 0
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373 | else:
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374 | return 1
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375 |
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376 | def calc_opponnets_value(self, bid: Bid):
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377 | if not bid:
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378 | return 0
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379 | # # own_utility = self.profile.getProfile().getUtility(bid)
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380 | # opponent_utility = self.opponent_model.get_predicted_utility(bid) # .getUtility(bid)
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381 | # return opponent_utility
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382 | value = 0
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383 | issues = bid.getIssues()
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384 | valUtil = [0.0]*len(issues)
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385 | isWeght = [0.0]*len(issues)
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386 | k = 0
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387 | for s in issues:
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388 | p = self.freqMap.get(s)
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389 | v = bid.getValue(s)
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390 | sumOfValues = 0
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391 | maxValue = 1
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392 | for vString in p[1].keys():
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393 | sumOfValues += p[1].get(vString)
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394 | maxValue = max(maxValue, p[1].get(vString))
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395 | valUtil[k] = float(p[1].get(vString)/maxValue)
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396 | mean = float(sumOfValues/len(p[1]))
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397 | for vString in p[1].keys():
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398 | isWeght[k] += math.pow(p[1].get(vString) - mean, 2)
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399 | isWeght[k] = 1.0/(math.sqrt(isWeght[k] + 0.1)/len(p[1]))
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400 | k += 1
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401 | sumOfwght = 0
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402 | for k in range(0, len(issues)):
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403 | value += valUtil[k] * isWeght[k]
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404 | sumOfwght += isWeght[k]
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405 | return value/sumOfwght
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406 |
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407 | def is_opponents_proposal_is_good(self, bid: Bid):
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408 | if bid == None:
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409 | return 0
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410 | value = self.calc_opponnets_value(bid)
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411 | index = int(((self.time_split - 1) / (1 - self.time_phase) * (self.progress.get(time() * 1000) - self.time_phase)))
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412 | if self.opThreshold != None:
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413 | self.opThreshold = max(1 - 2 * self.opThreshold[index], 0.2)
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414 | else:
|
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415 | self.opThreshold = 0.6
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416 | return value > self.opThreshold
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417 |
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418 | ###########################################################################################
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419 | ################################## Example methods below ##################################
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420 | ###########################################################################################
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421 |
|
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422 | def accept_condition(self, bid: Bid) -> bool:
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423 | if bid is None or self.opponent_name is None:
|
---|
424 | return False
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---|
425 | avg_max_utility = self.avg_opponent_utility[self.opponent_name]
|
---|
426 | if self.optimalBid is not None:
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---|
427 | maxValue = 0.95 * float(self.utilitySpace.getUtility(self.optimalBid))
|
---|
428 | else:
|
---|
429 | maxValue = 0.95
|
---|
430 | if self.isKnownOpponent(self.opponent_name):
|
---|
431 | avg_max_utility = self.avg_opponent_utility[self.opponent_name]
|
---|
432 | if self.alpha != 0:
|
---|
433 | self.utilThreshold = maxValue - (
|
---|
434 | maxValue - 0.6 * self.opponent_avg_utility - 0.4 * avg_max_utility + pow(self.std_utility, 2)) * (
|
---|
435 | math.exp(self.alpha * self.progress.get(time() * 1000) - 1) - 1) / (
|
---|
436 | math.exp(self.alpha) - 1)
|
---|
437 | return self.utilitySpace.getUtility(bid) >= self.utilThreshold
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---|
438 |
|
---|
439 | def find_bid(self) -> Bid:
|
---|
440 | # compose a list of all possible bids
|
---|
441 | domain = self.profile.getDomain()
|
---|
442 | all_bids = AllBidsList(domain)
|
---|
443 |
|
---|
444 | best_bid_score = 0.0
|
---|
445 | best_bid = None
|
---|
446 |
|
---|
447 | # take 500 attempts to find a bid according to a heuristic score
|
---|
448 | for _ in range(500):
|
---|
449 | bid = all_bids.get(randint(0, all_bids.size() - 1))
|
---|
450 | bid_score = self.score_bid(bid)
|
---|
451 | if bid_score > best_bid_score:
|
---|
452 | best_bid_score, best_bid = bid_score, bid
|
---|
453 |
|
---|
454 | return best_bid
|
---|
455 |
|
---|
456 | def score_bid(self, bid: Bid, alpha: float = 0.95, eps: float = 0.1) -> float:
|
---|
457 | """Calculate heuristic score for a bid
|
---|
458 |
|
---|
459 | Args:
|
---|
460 | bid (Bid): Bid to score
|
---|
461 | alpha (float, optional): Trade-off factor between self interested and
|
---|
462 | altruistic behaviour. Defaults to 0.95.
|
---|
463 | eps (float, optional): Time pressure factor, balances between conceding
|
---|
464 | and Boulware behaviour over time. Defaults to 0.1.
|
---|
465 |
|
---|
466 | Returns:
|
---|
467 | float: score
|
---|
468 | """
|
---|
469 | progress = self.progress.get(time() * 1000)
|
---|
470 |
|
---|
471 | our_utility = float(self.profile.getUtility(bid))
|
---|
472 |
|
---|
473 | time_pressure = 1.0 - progress ** (1 / eps)
|
---|
474 | score = alpha * time_pressure * our_utility
|
---|
475 |
|
---|
476 | if self.opponent_model is not None:
|
---|
477 | opponent_utility = self.opponent_model.get_predicted_utility(bid)
|
---|
478 | opponent_score = (1.0 - alpha * time_pressure) * opponent_utility
|
---|
479 | score += opponent_score
|
---|
480 |
|
---|
481 | return score
|
---|
482 |
|
---|
483 | def learn(self):
|
---|
484 | # not called...
|
---|
485 | return "ok"
|
---|
486 |
|
---|
487 | def isKnownOpponent(self, opponent_name):
|
---|
488 | return self.opponent_encounters.get(opponent_name, 0)
|
---|
489 |
|
---|
490 | def getSmoothThresholdOverTime(self, opponent_name):
|
---|
491 | if not self.isKnownOpponent(opponent_name):
|
---|
492 | return None
|
---|
493 | opponentTimeUtil = self.negotiation_data["opponent_util_by_time"]
|
---|
494 | smoothedTimeUtil = [0.0] * self.time_split
|
---|
495 |
|
---|
496 | for i in range(0, self.time_split, 1):
|
---|
497 | for j in range(max(i - self.smooth_width, 0), min(i + self.smooth_width + 1, self.time_split), 1):
|
---|
498 | smoothedTimeUtil[i] += opponentTimeUtil[j]
|
---|
499 | smoothedTimeUtil[i] /= (min(i + self.smooth_width + 1, self.time_split) - max(i - self.smooth_width, 0))
|
---|
500 | return smoothedTimeUtil
|
---|
501 |
|
---|
502 | def calculate_alpha(self, opponent_name):
|
---|
503 | alphaArray = self.getSmoothThresholdOverTime(opponent_name)
|
---|
504 | if alphaArray == None:
|
---|
505 | return self.default_alpha
|
---|
506 | for maxIndex in range(0, self.time_split, 1):
|
---|
507 | if alphaArray[maxIndex] > 0.2:
|
---|
508 | break
|
---|
509 | maxValue = alphaArray[0]
|
---|
510 | minValue = alphaArray[max(maxIndex - self.smooth_width - 1, 0)]
|
---|
511 |
|
---|
512 | if maxValue - minValue < 0.1:
|
---|
513 | return self.default_alpha
|
---|
514 | for t in range(0, maxIndex, 1):
|
---|
515 | if alphaArray[t] > (maxValue - self.opponent_decrease * (maxValue - minValue)):
|
---|
516 | break
|
---|
517 | calibratedPolynom = {572.83, -1186.7, 899.29, -284.68, 32.911}
|
---|
518 | alpha = calibratedPolynom[0]
|
---|
519 |
|
---|
520 | # lowers utility at 85% of the time why 85% ???
|
---|
521 | tTime = self.time_phase + (1 - self.time_phase) * (
|
---|
522 | maxIndex * (float(t) / self.time_split) + (self.time_split - maxIndex) * 0.85) / self.time_split
|
---|
523 | for i in range(1, len(calibratedPolynom), 1):
|
---|
524 | alpha = alpha * tTime + calibratedPolynom[i]
|
---|
525 |
|
---|
526 | return alpha
|
---|
527 |
|
---|
528 | def update_opponents_offers(self, op_sum, op_counts):
|
---|
529 | for i in range(0, self.time_split):
|
---|
530 | if op_counts[i] > 0:
|
---|
531 | self.negotiation_data["opponent_util_by_time"][i] = op_sum[i]/op_counts[i]
|
---|
532 | else:
|
---|
533 | self.negotiation_data["opponent_util_by_time"][i] = 0
|
---|
534 |
|
---|
535 | def update_negotiation_data(self):
|
---|
536 | if self.negotiation_data.get("aggreement_util") > 0:
|
---|
537 | newUtil = self.negotiation_data.get("aggreement_util")
|
---|
538 | else:
|
---|
539 | newUtil = self.opponent_avg_utility - 1.1 * math.pow(self.std_utility, 2)
|
---|
540 | self.opponent_avg_utility = (self.opponent_avg_utility * self.opponent_negotiations + newUtil) / (
|
---|
541 | self.opponent_negotiations + 1)
|
---|
542 | self.opponent_negotiations += 1
|
---|
543 | self.avg_opponent_utility[self.opponent_name] = self.opponent_avg_utility
|
---|
544 | self.negotiation_results.append(self.negotiation_data["aggreement_util"])
|
---|
545 | self.std_utility = 0.0
|
---|
546 | for util in self.negotiation_results:
|
---|
547 | self.std_utility += math.pow(util - self.opponent_avg_utility, 2)
|
---|
548 | self.std_utility = math.sqrt(self.std_utility / self.opponent_negotiations)
|
---|
549 |
|
---|
550 | opponent_name = self.negotiation_data["opponent_name"]
|
---|
551 |
|
---|
552 | if opponent_name != "":
|
---|
553 | if self.opponent_encounters.get(opponent_name):
|
---|
554 | encounters = self.opponent_encounters.get(opponent_name)
|
---|
555 | else:
|
---|
556 | encounters = 0
|
---|
557 | self.opponent_encounters[opponent_name] = encounters + 1
|
---|
558 |
|
---|
559 | if self.opponent_avg_max_utility.get(opponent_name):
|
---|
560 | avgUtil = self.opponent_avg_max_utility[opponent_name]
|
---|
561 | else:
|
---|
562 | avgUtil = 0.0
|
---|
563 | calculated_opponent_avg_max_utility = (float(avgUtil * encounters) + float(
|
---|
564 | self.negotiation_data["max_received_util"])) / (
|
---|
565 | encounters + 1)
|
---|
566 | self.opponent_avg_max_utility[opponent_name] = calculated_opponent_avg_max_utility
|
---|
567 |
|
---|
568 | if self.avg_opponent_utility[opponent_name]:
|
---|
569 | avgOpUtil = self.avg_opponent_utility[opponent_name]
|
---|
570 | else:
|
---|
571 | avgOpUtil = 0.0
|
---|
572 | calculated_opponent_avg_utility = (float(avgOpUtil * encounters) + float(
|
---|
573 | self.negotiation_data["opponent_util"])) / (
|
---|
574 | encounters + 1)
|
---|
575 | self.avg_opponent_utility[opponent_name] = calculated_opponent_avg_utility
|
---|
576 | if self.opponent_utility_by_time:
|
---|
577 | opponentTimeUtility = self.opponent_utility_by_time
|
---|
578 | else:
|
---|
579 | opponentTimeUtility = [0.0] * self.time_split
|
---|
580 |
|
---|
581 | newUtilData = self.negotiation_data.get("opponent_util_by_time")
|
---|
582 | if opponentTimeUtility[0] > 0.0:
|
---|
583 | ratio = ((1 - self.new_weight) * opponentTimeUtility[0] + self.new_weight * newUtilData[0] /
|
---|
584 | opponentTimeUtility[0])
|
---|
585 | else:
|
---|
586 | ratio = 1
|
---|
587 | for i in range(0, self.time_split, 1):
|
---|
588 | if newUtilData[i] > 0:
|
---|
589 | valueUtilData = (
|
---|
590 | (1 - self.new_weight) * opponentTimeUtility[i] + self.new_weight * newUtilData[i])
|
---|
591 | opponentTimeUtility[i] = valueUtilData
|
---|
592 | else:
|
---|
593 | opponentTimeUtility[i] *= ratio
|
---|
594 | self.negotiation_data["opponent_util_by_time"] = opponentTimeUtility
|
---|
595 | self.opponent_alpha[opponent_name] = self.calculate_alpha(opponent_name)
|
---|
596 |
|
---|